A Fractional-Order Model Reference Adaptive Control (FO-MRAC) strategy based on PD control is proposed to realize attitude control of quadrotor UAVs,which is affected by undervoltage batteries and variation of rotational inertia.Firstly,by analyzing the nonlinear motion equation of a UAV with motor module,a control model containing uncertain time-varying factors is constructed.Then,three attitude control laws are designed for the pitch,roll and yaw channels respectively.The stability of the system is proved by Lyapunov theorem.Finally,comparative simulation experiments with the traditional PD control and the ordinary MRAC are designed,which illustrate the superiority of the proposed method.The method can effectively eliminate errors,improve dynamic performance and stability of the system.

UAV swarm operations are gradually becoming a mainstream mode of air combat in the future,which poses severe threats and challenges to air defense operations.The reasonable deployment of air defense weapons in advance has a direct impact on the overall performance of operations against UAV swarm.From the perspective of the two sides in air defense operations,this paper comprehensively analyzes the autonomous intelligence of UAV swarm and the main factors affecting air defense deployment.Besides,the confrontation process between UAV swarm and air defense weapons is quantified,and an air defense deployment model based on nested Particle Swarm Optimization (PSO) algorithm is proposed.The simulation example verifies that the model taking the UAV swarm threat index as indicators can be used for optimizing the deployment of air defense weapons,and has high reference value for the researches on decision-making of combat against UAV swarm.

Sparrow Search Algorithm (SSA) has better performance than other biomimetic algorithms. However,its convergence accuracy is not high enough,and it is prone to fall into local optimum under complex multi-modal functions. In order to overcome these defects,this paper proposes an improved multi-strategy SSA based on K-means. The algorithm adopts multiple search strategies. Firstly,the initial population is clustered by K-means to accelerate information exchange within the population. Then,the sine and cosine search strategy is used to update the position of the followers,and the adaptive local search strategy is used to update the optimal individual,so as to find a more reliable feasible solution and improve convergence accuracy and optimization ability. The dual-strategy SSA is compared with the two single-strategy SSAs through 10 test functions,and it is verified that the introduction of the strategies can effectively improve the optimization ability of SSA. The two single-strategy SSAs have stronger optimization ability,and the dual-strategy SSA has weaker optimization ability. Finally,the three SSAs are applied to the LQR control of active suspension.The experimental results show that the optimization effect of the dual-strategy SSA is not ideal,while the two single-strategy SSAs have significant optimization effect,which can improve the performance of active suspension.

The imaging methods based on Compressive Sensing (CS) for Multiple-Input Multiple-Output (MIMO) radar have the advantage of high resolution.However,in practical applications,these methods suffer from high computational complexity,which cannot meet the timeliness requirements of moving-target imaging.To solve this problem,a novel 3D high-resolution imaging method for MIMO radar based on Non-Uniform Fast Fourier Transform (NUFFT) and Fast Iterative Soft Thresholding Algorithm (FISTA) is proposed.This method uses NUFFT to approximate the matrix-vector multiplications in FISTA with high precision,which can reduce imaging time and requirements on the systems memory while ensuring high-resolution imaging.Simulation results have verified the feasibility of the proposed method and its advantages over the existing classical methods.

When C4KISR is attacked on the battlefield,how to quickly recover from its failures and reduce performance loss is the key to the robustness and resilience of the system.On the basis of analyzing structural and functional characteristics of C4KISR,a double-layer interdependent network model of C4KISR is constructed and its failure mechanism is discussed.Then,a dynamic failure-to-recovery model of C4KISR is established,and the corresponding resilience strategy is proposed.Finally,a certain C4KISR is set as an example for simulation,and the influence of parameters on the networks performance and the systems resilience is discussed.Simulation results have proved the rationality and effectiveness of the proposed model. This research provides a basis for the design of resilience strategies,and provides useful reference for the enhancement of battlefield survivability of C4KISR and its capability to support systematic combat.

The cognitive radar network has shown significant advantages in multi-target tracking.This paper presents a bandwidth and dwelling time allocation algorithm based on Posterior Cramer-Rao Lower Bound (PCRLB).The influence of bandwidth and dwelling time on tracking accuracy is analyzed,and a model describing their relationship is constructed.A PCRLB-based optimization objective function with constraints is designed,and the joint allocation of dwelling time and bandwidth is realized by Particle Swarm Optimization (PSO).The simulation results show that,compared with the method of equal allocation of resource,the proposed method can not only save time and bandwidth resources,but also improve the tracking performance of radar.

Vector Tracking Loop (VTL) is one of the most promising signal tracking technologies of Global Navigation Satellite System (GNSS) receiver technologies. In the traditional GNSS receiver, Phase Lock Loop (PLL) is employed to complete carrier signal tracking, and then Least Squares Method (LSM) or Kalman Filter (KF) is utilized to estimate navigation information. In contrast, VTL utilizes a KF to complete signal tracking and navigation estimation, which has better signal tracking abilities. In order to improve VTL positioning accuracy, Graph Optimization (GO) based VTL is proposed in this paper. In this method, GO is used to replace KF. Compared with KF, GO regards the state and measurement equations of VTL filter as constraints, whose optimal solution is obtained by Levenberg-Marquardt algorithm, so that the optimal navigation estimation is obtained. Field tests show that the standard deviation of 3D position errors is reduced respectively by 40.5%, 50.9% and 41.7%, and the mean values of 3D position errors are decreased respectively by 43.0%, 54.9% and 34.7%.

SVM-based military target recognition has the characteristics of fast speed and good robustness, and accurate recognition relies on image feature selection.In field environment,the targets image is affected by complex interference factors.In view of the problem,a compound feature selection method is proposed.The visible and infrared images obtained are evaluated,in which the discriminative ones are selected and segmented based on Soft method.Then,moment-invariant features are extracted and feature fusion is made to provide reliable feature vectors for SVM.

Cyber-physical systems often suffer from false data injection attacks and limited communication bandwidth.To solve the problem,a robust security-event-triggered sliding mode controller is proposed.First,according to the threshold information of anomaly detection mechanism,the upper bound information of false data injection attacks is obtained.Second,based on the estimated upper bound information of the attacks,the event-triggering mechanism is introduced to design a robust security-event-triggered sliding mode controller.The effectiveness of the proposed controller is proved by Lyapunov stability theory,and it is also proved that there is no Zeno phenomenon in the designed controller.Finally,the simulation results show that the proposed controller can ensure the systems safe and stable operation with the minimum event-triggering times,thus saving the communication resources.

As for actuator failure of quad-rotor UAV formation when switching topology,a fault-tolerant multi-agent consistency control method based on fuzzy system is proposed.Firstly,the error model and actuator failure model of multi-agent quad-rotor UAV are established.Then,the fuzzy system is used to approximate the nonlinear part of the quad-rotor UAV,and an adaptive law is designed to estimate actuator failure and uncertainty of external disturbances.Finally,the stability of the fault-tolerant consistency control law is analyzed,which proves that the tracking error of each of the UAVs is uniformly bounded.The simulation results show that the designed fault-tolerant consistency control law has good stability,robustness and tracking accuracy with the maximum tracking error of only 0.3°,which realizes fault-tolerant UAV consistency control under nonlinear uncertainty,external disturbances and actuator failure when switching topology.

In order to improve the response speed and accuracy of a certain follow-up load simulator,a study on neural network Sliding Mode Control (SMC) is conducted.Based on the model of the servo system,Non-singular Terminal Sliding Mode Controller (NTSMC) responds to its nonlinear dynamic changes.The output of nonlinear quantitative Cerebellar Model Articulation Controller (CMAC) is used to compensate for SMC output,and the gradient descent method is used to update its weight.Nonlinear quantitative CMAC has the advantages of strong generalization ability and fast convergence,and NTSMC has the advantages of strong robustness.The combination of the two can effectively reduce the influence of nonlinear factors in the load simulator.Simulation experiments show that the proposed method can ensure the systems stability,accelerate dynamic response and improve the control accuracy.

The traditional Kernel Correlation Filtering (KCF) tracking algorithm lacks the ability to deal with target occlusion.To solve the problem,occlusion judgment indexes and an improved algorithm with adaptive model updating are proposed.Two indexes,maximum response value and the number of low response points,are used to comprehensively determine whether there is occlusion,and then the models learning rate is adjusted adaptively,so as to avoid inaccurate tracking in the presence of occlusion.The image sequences with occlusion are selected from OTB2015 data set to verify the performance of the algorithm.Compared with that of the traditional KCF algorithm for tracking under occlusion,the accuracy is increased by 15.12%,and the success rate is increased by 14.7%.The experimental results show that the improved algorithm can accurately track targets when there is occlusion with higher accuracy and stronger robustness.

In view of the relationship between each Degree of Freedom (DOF) of the 3DOF helicopter system,the helicopter system is divided into an actuated subsystem and a non-actuated subsystem.The mathematical model of the 3DOF helicopter is established,which depicts the possible external disturbances and sensor failures of the non-actuated subsystem.A nonlinear extended disturbance observer is designed to eliminate the adverse effects caused by external disturbances and sensor failures,and a high-order sliding mode controller is designed to realize tracking control of the actuated subsystem.The stability of the system is proved by Lyapunov theory.Finally,the experimental results illustrate that the control scheme has good effects on the 3DOF helicopter with sensor failures.

The classical theories of reliability parameter sensitivity analysis based on probability theory and fuzzy mathematics cannot directly solve the uncertainty of subjective,random and fuzzy variables.To solve the problem,the uncertainty theory is introduced,and uncertain variables are utilized to uniformly represent the subjective,random and fuzzy variables,so as to satisfy the duality of randomness and subadditivity of fuzziness.Moreover,new formulas of reliability parameter sensitivity analysis are derived based on Cornell uncertain reliability index.Numerical examples have demonstrated the effectiveness of the proposed method, which can provide a reference for reliability optimization.

Traditional methods for jamming effect evaluation in communication countermeasure cannot be applied to real-time evaluation in wartime.To solve the problem,an online evaluation method based on GA-ELM is proposed.Link-16 is taken as the research object.Firstly,the parameters of its anti-jamming behavior are analyzed and selected to build a sample library.Then,ELM is used to learn the sample parameters after classification,and the improved GA is used to optimize ELM,so that the GA-ELM model is obtained.Experimental results show that GA-ELM can obtain online evaluation results of jamming effect based on the parameters of anti-jamming behavior.The new method overcomes the shortcoming of the traditional methods, and is more close to actual combat.

Multi-Input Multi-Output (MIMO) radar has flexible working modes,which has shown outstanding potential in such areas as anti-interception and weak target detection.Its advantage of multiple transmission and reception signals also brings certain limitations,that is,each channel is required to transmit orthogonal waveforms.Therefore,the design of orthogonal waveforms has become a core area of MIMO radar systems.In this paper,a modified Jaya algorithm is proposed to design waveforms with constant amplitude and arbitrary phase,which display good aperiodic auto-correlation and cross-correlation properties.Based on the framework of Jaya algorithm,the crossing and mutation operators of Genetic Algorithm (GA) is incorporated into the individuals update function to design discrete-phase waveforms,and Hamming Scan algorithm is applied to find the best sequence from the output of Jaya algorithm.Numerical simulations have verified the effectiveness of the proposed algorithm.

At present,when a single soldier uses barrel-type munitions,the hit probability improvement is mostly based on the preposition method,which depends on the shooters estimation of target motion information.Target motion information mainly includes the angular rate and yaw angle of the targets lateral motion relative to the shooter,as well as the targets speed in the direction of weapon/target connecting line (radial direction).Based on this,a scheme of real-time measurement of target motion information using MIMU fixed on the launcher is proposed,in which Kalman filter algorithm is used to suppress noise in sensor data,and the equivalent rotation vector algorithm is used to calculate the attitude angles.A test prototype is designed,and the feasibility of the proposed scheme is verified through tests.The test results show that,the maximum deviation of lateral angular rate is 0.001 5 (°)/s and 0.002 5 (°)/s at the firing angles of 5° and 28° respectively,so that the shooter can obtain accurate target motion information,thereby improving shooting accuracy and alleviating operation burden.

An improved velocity obstacle method is proposed to improve the ROVs autonomous ability to avoid dynamic obstacles in complex underwater environment.The motion uncertainty of dynamic obstacles in velocity space is transformed into position uncertainty.By analyzing the risk of collision and the velocity obstacle of the maximum expansion circle,the starting and ending time of obstacle avoidance is determined.In order to select the optimal velocity for obstacle avoidance,an objective function considering three factors,i.e.,safety,the tendency of moving towards the target and speed change quantity,is designed.Finally,the feasibility and effectiveness of the method are verified by simulation.

Under the influence of the systems loads and external torque disturbances,the accuracy of mobile robot path following control is decreased.To solve the problem,a cascaded control strategy for mobile robot path following based on disturbance observer is proposed.On the level of kinematics,based on backstepping methods,position errors are introduced into angular errors,and a globally-convergent speed control law is designed.On the level of dynamics,a nonlinear sliding mode torque controller is designed to follow the planned speed.At the same time,a super-twisting disturbance observer is used to observe the lumped disturbances,and its observation value is introduced into the torque controller to reduce the influence of lumped disturbances on tracking accuracy.The simulation results show that the proposed method has good path following performance and anti-interference abilities.

Traditional fault diagnosis of aircrafts rudder surface has poor effects and weak generalization ability.To solve the problem,a fault diagnosis model suitable for civil aircrafts elevators is built based on Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM) classifier.The model learns the original fault data layer by layer to extract fault features and identify fault types,and uses SVM instead of Softmax function to classify the faults.The model is compared with the traditional models of CNN network and Deep Belief Network (DBN).The experimental results show that the proposed model has the highest accuracy for elevator fault recognition,which can be above 99%.In order to directly observe the differences of the three models in feature representation,the recognition results are visualized through dimensionality reduction (T-SNE).Through the visualized graphs,it can be seen that the CNN-SVM model has significant clustering effects.Finally,noise is added to the data set,and it is verified that the proposed model has better anti-noise ability,generalization ability and reinforcement learning ability than the other two models.

In the traditional matrix analysis of the criticality of aviation products,hazard degree calculation has low accuracy and efficiency.To solve the problem,a criticality assessment method based on a planar cloud model is proposed.Firstly,the planar cloud model is used to analyze the criticality of a single failure mode and solve the criticality level of a single failure mode.Then,gray relation and clustering analysis as well as information entropy are used for weighting the failure modes.The criticality levels of various failure modes are clustered as the criticality level of the whole product.Finally,the correctness of the proposed method is verified by a case analysis of the elevator control subsystem of a certain aircraft.

Laser is an important component of airborne photoelectric long-range detection system,whose performance directly determines the systems application effect.Firstly,based on the laser ranging equation, different detection systems and the requirements of dual-wavelength detection are analyzed.Then,the semiconductor pumped solid-state laser is studied,and different implementation methods of dual wavelength are analyzed.Finally,the output of high-energy dual-wavelength laser is realized through experimental verification.The experimental results show that the output of dual-wavelength laser of 1.064 μm and 1.57 μm with high repetition rate and narrow pulse width can be realized by the technologies of electro-optic active-Q-switching and LD side-pumped resonator.

In thick fog conditions,the medium with uneven density will cause light scattering,reflection and attenuation in light transmission process,resulting in serious image degradation,so there is difficulty in obtaining image information from UAV aerial photography.In order to solve this problem,a bilateral-filtering transmittance optimization algorithm based on boundary constraints is proposed for image defogging.Firstly,the colors of the image are corrected to enhance the brightness of the image.Secondly,according to the inherent boundary constraints of the transmission function,the boundary constraints are constructed and then combined with context regularization,and the initial transmittance of the image is obtained and then the transmittance is refined through repeated calculation.Then,the refined transmittance is Gaussian-weighted to minimize noise interference,and the optimal transmittance is obtained by bilateral filter.Finally, the global atmospheric light of the image is evaluated and the dark channel theory is used to defog the image,so that a sharpened image is obtained.Based on subjective visual effects and objective experimental data analysis,it is proved that the proposed method can effectively remove the fogging phenomenon in degraded images,and has a significant advantage over the existing novel image defogging algorithms.